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How does a data mesh drive enterprise-wide value for knowledge workers?.

by Helen Patenall

Data mesh powered by an insight engine is a powerful combination when it comes to discovering, interconnecting, and enriching information spread across your enterprise. The benefit? Real-time data insight delivered directly to your workforce to propel your business forward.

View data as a product to connect your workforce to data insight

Artificial intelligence has the potential to make enterprise search and intelligent search increasingly effective, customised, and collaborative for knowledge workers. But when it comes to deriving real enterprise-wide value from mountains of data, key challenges and information overwhelm still hold too many businesses back. This is where a data mesh approach can really help.

Siloed data, disconnected repositories, and separate systems are just some of the obstacles that prevent your workforce from finding the information they need to achieve your business goals. Whether they’re searching for data hidden away in another user’s personal drive, information put beyond reach by inaccessible permissions, or trying to find something that simply does not exist, these searches take up valuable time, double up workloads, and duplicate data creation. And these information processing bottlenecks slow down your business operations and create a ceiling effect on your scalability. There’s a far better way.

Instead, view your data as a product your business produces. And your workplace filled with smart knowledge workers and cross-functional collaborative teams. All creating, sharing, and expanding good data and readily consumable information to support a myriad of business goals. An insightful barrier- and risk-free enterprise search system stocked with good data that your workforce can democratically tap into for fast decisions based on credible content is far more forward-thinking.

If you see your data as a product – and put in place accessible and collaborative solutions that interconnect it all – you’ve made every nugget of your data exponentially more robust and valuable. Leveraging your data in this way makes it infinitely greater than the sum of its parts, throwing fresh new insight and value directly into the hands of your workforce.

Data insight is the perfect solution to your real-world challenges

Whether your compliance knowledge workers are handling personal data, your engineering knowledge workers are maintaining physical assets, your sales knowledge workers are looking for new market opportunities, or your fraud knowledge workers are investigating misconduct at regulated firms, making all your data products readily accessible in real-time means your entire workforce can extract the live data they need, exactly when they need it, to find instant solutions to your real-world challenges.

In doing so, your compliance knowledge workers can find all the information they need from across the enterprise to respond to every subject access request (SAR) or right to be forgotten (RTBF) request at top speed, your sales knowledge workers can find unstructured information to unlock brand-new insights, your engineering knowledge workers can find all the data they need for a specific asset, and your fraud knowledge workers can find the data they need to track patterns of behaviour across datasets.

These knowledge workers can now self-serve and accumulate infinite value from your interconnected data, sharing insightful knowledge across the board for responsive business actions in line with changing market and environmental situations. Quickly. And by connecting more users to these insights, you’re supporting cross-functional team collaboration to manage your information at scale and unlock added value for your business.

Expanding the reach, scale, and impact of your data to a whole new visionary level sounds ideal. But, in practice, how can you make your data work across your workforce in this way?

How to drive your enterprise forward with a data mesh

First, you need to interconnect, enrich, and discover all your information. At Aiimi, we call this approach our data mesh. So, what is data mesh? As Forrester’s Michele Goetz neatly puts it:

“Data mesh models data as a twin of the business, in the language of the business. This makes it possible to work with data in a declarative fashion and simplify integration of data components with application components. Think how rapidly you could create and scale insights-driven solutions with a common framework.”

At Aiimi, our data mesh approach is delivering tangible data-driven results. Here’s a simple analogy. We think of businesses as vast data enterprises filled with diverse data sources. Often spread out, some are expanding and new ones are being built all the time. Change is constant. We think of our data mesh like a road map, creating a completely interconnected overview of this data enterprise. Without exception.

By linking all these data sources – internal and external systems, repositories, and applications – our data mesh makes all structured and unstructured data discoverable and accessible to your workforce. This means that all your information can be put to work across your business, instantly elevating it to a whole new visionary level. And the best bit? No one has to move one iota of anything. All your evolving data sources can stay exactly where they are, wherever they live in your data enterprise because it’s all road mapped.

When all your business domains are working within one consistent framework, the added benefit is one set of policies and procedures. This means you can keep compliance in check and reduce opportunities for a potential data breach. It also adds a new level of trust and confidence in your shared data. Federated data governance makes it easier for your teams to see who’s using what data and how, enhancing its visibility and transparency to safeguard your personal data and sensitive information. For example, at Aiimi we also use a data mesh to secure data privacy and improve compliance through federated data governance. So, how do you create a data mesh?

How Aiimi’s Insight Engine powers a data mesh

Powering a data mesh to crawl, search, and discover your data products to supply one single unified user experience across your enterprise is the Aiimi Insight Engine. Working perfectly in tandem, the insight engine automatically enriches all your information to construct interconnected data products at speed. AI also powers machine learning models and self-learning algorithms to understand user intent better. In this way, it returns accurate, relevant, and personalised insightful results. Fast. Insight engines move through key four stages to supply these answers.

  1. Discovery stage – insight engines automatically crawl all your on-premises and cloud repositories, sources, and systems to capture a live index of all your structured and unstructured data. You don’t have to move or migrate anything for this to happen. Everything stays where it is, and everything is crawled.
  2. Enrichment stage – insight engines use machine learning techniques like named-entity recognition, optical character recognition, and image recognition, plus natural language processing and geotagging, to automatically enrich all this information with added structure and context.
  3. Repository stage – insight engines store an index of all this enriched information, along with its security information and other configuration data, ready to power search and insight requirements. Crucially, all this newly enriched and indexed information still lives in its existing location. Nothing is moved, relocated, or migrated.
  4. Gateway stage – securing this repository is the insight engine’s multi-factor authentication and single sign-on, respecting all your existing access permissions.

Combined, these four stages create an enterprise-wide data mesh that interconnects all your newly enriched data. Your workforce can now use all your enriched and interconnected data products to derive the insight they need to resolve business challenges easily and quickly.

How to glean data insight for greater enterprise-wide value

As these data assets augment, your knowledge workers will get to benefit from even richer insight pickings to drive productivity. For example, when your compliance knowledge workers are processing Subject Access Requests or Right to be Forgotten requests, they can easily highlight all personal data entities and audit all relevant interconnected files to deal with any potential data breach, ending access concerns.

Whereas your sales knowledge workers can view relationships or shared topics between data records, your engineering knowledge workers can see relationships between physical assets, and your fraud knowledge workers can drill down into relationships between different entities (people, places, and organisations) ordered chronologically, geographically, or analytically.

It’s easy to see how a data mesh approach combined with the insight engine’s capabilities will help your workforce reap a lot more value from your data products.

How to unlock and visualise new data insight at speed with the Aiimi Insight Engine

These new data insights can also be consumed in real-time via powerful timeline or histogram visualisations, geo-tagged map views, and network diagrams, among other software features, to enable your workforce to achieve a whole lot more, regardless of their department. Let’s explore some of these valuable Aiimi Insight Engine features in more detail.

Using the Data Map feature, your workforce can view and access curated data within the context of the data’s respective domain area, connecting the physical world of data with the diverse functions of your business. Using this tool, your users can quickly focus on a specific business area when looking for data.

For example, using Data Map, your recruitment knowledge workers can quickly drill down into the human resources’ recruitment domain area of your business when they’re searching for a document on an employee or a job profile to simplify their search.

When it comes to visualising interconnections between diverse data records, the Network Diagrams feature highlights new insightful relationships that your users can analyse to work out exactly why and how they’re related. This can open new avenues of insightful exploration.

For example, if a user is viewing information on a specific individual, the Network Diagram will visualise all connected individuals, affiliated departments, and associated organisations to uncover fresh new data insight. Or if a finance knowledge worker is viewing invoices, the Network Diagram will show which customers are linked to those invoices for added context to facilitate decision-making and authorisation.

The Timeline Visualisation feature works in much the same way, but instead of highlighting relationships, it visualises relevant and notable events related to a user’s search result. For example, showing when an item was created or changed, or when its ownership changed hands. A knowledge worker within the logistics team could use Timeline Visualisations to track an item’s transition through the production lifecycle to discover potential bottlenecks and make insightful improvements.

Similarly, the Histogram Visualisation feature shows time periods when activity on a data record spiked, within the context of a user’s specific search or area of interest, for a more perceptive and personalised experience. By visually bringing attention to trends, patterns, and outliers, your compliance knowledge workers can easily share new insights with other teams, and spot suspicious or non-compliant behaviour.

Or a knowledge worker in your manufacturing or engineering teams can easily spot high activity spikes to uncover exactly when your customers most often report a faulty item or return a purchase. Equipped with this knowledge, your teams can implement new measures to counteract these incidents.

When it comes to finding connections between data based on geographical location, the Map View feature intelligently plots information relating to a user’s specific topic of interest on a geographical map to highlight insightful relationships. This is particularly useful for suppliers looking to find out where their customers are located. For example, your marketing knowledge workers can use data held in your customer records management (CRM) systems to plot customers geographically, and then overlay this Map View with related files like contractual or financial information.

Watch this video to see how the Aiimi Insight Engine’s Timeline, Histogram, and Map View deliver a visual Search and Discovery user experience for your knowledge workers.

Where will a data mesh and insight engine take you next?

Powerful insight engine features like these will enable your workforce to discover deeper insight hidden in your data and information to generate fresh ideas, spot new areas of opportunity, resolve key challenges, and make fast business decisions in response to rapidly changing market conditions.

And these features are just the tip of the iceberg, with new capabilities being rolled out all the time. We predict that this combined data mesh and insight engine solution will become universally adopted as more businesses come to realise the power of connecting people to insight.

Read our latest eGuide – THE INSIGHT OPPORTUNITY: Uncovering the Next Generation of Information Intelligence – to find out how our data mesh and insight engine work together to unlock insight.

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